Senior Data Modeler

Qualifications
Extensive experience in developing and implementing conceptual, logical, and physical data models.
Proven ability to work independently and collaboratively in a fast-paced environment, taking ownership of complex data initiatives.
Expert-level experience in writing and optimizing SQL queries for data analysis, profiling, and integrity checks.
In-depth knowledge of various data platforms, including RDBMS, Operational Data Stores (ODS), Data Marts, and Data Lakes.
Experience with dimensional modeling (e.g., star schema, snowflake schema) and techniques for both relational and NoSQL platforms.
Strong understanding of core data management concepts, including metadata management, data warehousing, and data lineage.
Excellent communication and documentation skills, with the ability to translate complex data design concepts to both technical and non-technical stakeholders.

Job Responsibilities
Design and Model Development: Lead the development of the conceptual, logical, and physical data models for all enterprise data systems, including RDBMS, ODS, Data Marts, and Data Lakes.
Platform Implementation: Oversee the successful implementation of data models on target platforms (SQL/NoSQL) and ensure the translation of business requirements into structured data designs.
Governance and Standards: Define, govern, and enforce data modeling and design standards, tools, and best practices for the enterprise data models.
Architecture Oversight: Oversee and govern the expansion of existing data architecture, ensuring alignment with strategic data management goals.
Data Quality & Analysis: Execute complex SQL queries for data analysis, data profiling, and validation to ensure referential integrity and data quality are consistently maintained.
Collaboration: Work with business and application/solution teams to document data flows and develop detailed data models that support analytical and operational needs.
Risk Mitigation: Proactively identify and articulate issues and challenges in data design to reduce risks and ensure data structure scalability and optimization.
Optimization: Drive the optimization of data query performance across platforms via model tuning and best practices implementation.

Responsibilities

Design and Model Development: Lead the development of the conceptual, logical, and physical data models for all enterprise data systems, including RDBMS, ODS, Data Marts, and Data Lakes.

Platform Implementation: Oversee the successful implementation of data models on target platforms (SQL/NoSQL) and ensure the translation of business requirements into structured data designs.

Governance and Standards: Define, govern, and enforce data modeling and design standards, tools, and best practices for the enterprise data models.

Architecture Oversight: Oversee and govern the expansion of existing data architecture, ensuring alignment with strategic data management goals.

Data Quality & Analysis: Execute complex SQL queries for data analysis, data profiling, and validation to ensure referential integrity and data quality are consistently maintained.

Collaboration: Work with business and application/solution teams to document data flows and develop detailed data models that support analytical and operational needs.

Risk Mitigation: Proactively identify and articulate issues and challenges in data design to reduce risks and ensure data structure scalability and optimization.

Optimization: Drive the optimization of data query performance across platforms via model tuning and best practices implementation.